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mmedit.models.base_models.base_gan

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Classes

BaseGAN

Base class for GAN models.

Attributes

ModelType

mmedit.models.base_models.base_gan.ModelType[source]
class mmedit.models.base_models.base_gan.BaseGAN(generator: ModelType, discriminator: Optional[ModelType] = None, data_preprocessor: Optional[Union[dict, mmengine.Config]] = None, generator_steps: int = 1, discriminator_steps: int = 1, noise_size: Optional[int] = None, ema_config: Optional[Dict] = None, loss_config: Optional[Dict] = None)[source]

Bases: mmengine.model.BaseModel

Base class for GAN models.

Parameters
  • generator (ModelType) – The config or model of the generator.

  • discriminator (Optional[ModelType]) – The config or model of the discriminator. Defaults to None.

  • data_preprocessor (Optional[Union[dict, Config]]) – The pre-process config or GenDataPreprocessor.

  • generator_steps (int) – The number of times the generator is completely updated before the discriminator is updated. Defaults to 1.

  • discriminator_steps (int) – The number of times the discriminator is completely updated before the generator is updated. Defaults to 1.

  • ema_config (Optional[Dict]) – The config for generator’s exponential moving average setting. Defaults to None.

property generator_steps: int[source]

The number of times the generator is completely updated before the discriminator is updated.

Type

int

property discriminator_steps: int[source]

The number of times the discriminator is completely updated before the generator is updated.

Type

int

property device: torch.device[source]

Get current device of the model.

Returns

The current device of the model.

Return type

torch.device

property with_ema_gen: bool[source]

Whether the GAN adopts exponential moving average.

Returns

If True, means this GAN model is adopted to exponential

moving average and vice versa.

Return type

bool

static gather_log_vars(log_vars_list: List[Dict[str, torch.Tensor]]) Dict[str, torch.Tensor][source]

Gather a list of log_vars. :param log_vars_list: List[Dict[str, Tensor]]

Returns

Dict[str, Tensor]

_init_loss(loss_config: Optional[Dict] = None) None[source]

Initialize customized loss modules.

If loss_config is a dict, we allow kinds of value for each field.

  1. loss_config is None: Users will implement all loss calculations

    in their own function. Weights for each loss terms are hard coded.

  2. loss_config is dict of scalar or string: Users will implement all

    loss calculations and use passed loss_config to control the weight or behavior of the loss calculation. Users will unpack and use each field in this dict by themself.

    loss_config = dict(gp_norm_mode=’HWC’, gp_loss_weight=10)

  3. loss_config is dict of dict: Each field in loss_config will

    used to build a corresponding loss module. And use loss calculation function predefined by BaseGAN to calculate the loss.

    loss_config = dict()

Example

loss_config = dict(

# BaseGAN pre-defined fields gan_loss=dict(type=’GANLoss’, gan_type=’wgan-logistic-ns’), disc_auxiliary_loss=dict(

type=’R1GradientPenalty’, loss_weight=10. / 2., interval=2, norm_mode=’HWC’, data_info=dict(

real_data=’real_imgs’, discriminator=’disc’)),

gen_auxiliary_loss=dict(

type=’GeneratorPathRegularizer’, loss_weight=2, pl_batch_shrink=2, interval=g_reg_interval, data_info=dict(

generator=’gen’, num_batches=’batch_size’)),

# user-defined field for loss weights or loss calculation my_loss_2=dict(weight=2, norm_mode=’L1’), my_loss_3=2, my_loss_4_norm_type=’L2’)

Parameters

loss_config (Optional[Dict], optional) – Loss config used to build loss modules or define the loss weights. Defaults to None.

noise_fn(noise: mmedit.utils.typing.NoiseVar = None, num_batches: int = 1)[source]

Sampling function for noise. There are three scenarios in this function:

  • If noise is a callable function, sample num_batches of noise with passed noise.

  • If noise is None, sample num_batches of noise from gaussian distribution.

  • If noise is a torch.Tensor, directly return noise.

Parameters
  • noise (Union[Tensor, Callable, List[int], None]) – You can directly give a batch of label through a torch.Tensor or offer a callable function to sample a batch of label data. Otherwise, the None indicates to use the default noise sampler. Defaults to None.

  • num_batches (int, optional) – The number of batches label want to sample. If label is a Tensor, this will be ignored. Defaults to 1.

Returns

Sampled noise tensor.

Return type

Tensor

_init_ema_model(ema_config: dict)[source]

Initialize a EMA model corresponding to the given ema_config. If ema_config is an empty dict or None, EMA model will not be initialized.

Parameters

ema_config (dict) – Config to initialize the EMA model.

_get_valid_model(batch_inputs: mmedit.utils.typing.ForwardInputs) str[source]

Try to get the valid forward model from inputs.

  • If forward model is defined in batch_inputs, it will be used as forward model.

  • If forward model is not defined in batch_inputs, ‘ema’ will returned if :property:`with_ema_gen` is true. Otherwise, ‘orig’ will be returned.

Parameters

batch_inputs (ForwardInputs) – Inputs passed to forward().

Returns

Forward model to generate image. (‘orig’, ‘ema’ or

’ema/orig’).

Return type

str

forward(inputs: mmedit.utils.typing.ForwardInputs, data_samples: Optional[list] = None, mode: Optional[str] = None) mmedit.utils.typing.SampleList[source]

Sample images with the given inputs. If forward mode is ‘ema’ or ‘orig’, the image generated by corresponding generator will be returned. If forward mode is ‘ema/orig’, images generated by original generator and EMA generator will both be returned in a dict.

Parameters
  • batch_inputs (ForwardInputs) – Dict containing the necessary information (e.g. noise, num_batches, mode) to generate image.

  • data_samples (Optional[list]) – Data samples collated by data_preprocessor. Defaults to None.

  • mode (Optional[str]) – mode is not used in BaseGAN. Defaults to None.

Returns

A list of EditDataSample contain generated results.

Return type

SampleList

val_step(data: dict) mmedit.utils.typing.SampleList[source]

Gets the generated image of given data.

Calls self.data_preprocessor(data) and self(inputs, data_sample, mode=None) in order. Return the generated results which will be passed to evaluator.

Parameters

data (dict) – Data sampled from metric specific sampler. More detials in Metrics and Evaluator.

Returns

Generated image or image dict.

Return type

SampleList

test_step(data: dict) mmedit.utils.typing.SampleList[source]

Gets the generated image of given data. Same as val_step().

Parameters

data (dict) – Data sampled from metric specific sampler. More detials in Metrics and Evaluator.

Returns

Generated image or image dict.

Return type

List[EditDataSample]

train_step(data: dict, optim_wrapper: mmengine.optim.OptimWrapperDict) Dict[str, torch.Tensor][source]

Train GAN model. In the training of GAN models, generator and discriminator are updated alternatively. In MMEditing’s design, self.train_step is called with data input. Therefore we always update discriminator, whose updating is relay on real data, and then determine if the generator needs to be updated based on the current number of iterations. More details about whether to update generator can be found in should_gen_update().

Parameters
  • data (dict) – Data sampled from dataloader.

  • optim_wrapper (OptimWrapperDict) – OptimWrapperDict instance contains OptimWrapper of generator and discriminator.

Returns

A dict of tensor for logging.

Return type

Dict[str, torch.Tensor]

_get_gen_loss(out_dict)[source]
_get_disc_loss(out_dict)[source]
train_generator(inputs: dict, data_samples: List[mmedit.structures.EditDataSample], optimizer_wrapper: mmengine.optim.OptimWrapper) Dict[str, torch.Tensor][source]

Training function for discriminator. All GANs should implement this function by themselves.

Parameters
  • inputs (dict) – Inputs from dataloader.

  • data_samples (List[EditDataSample]) – Data samples from dataloader.

  • optim_wrapper (OptimWrapper) – OptimWrapper instance used to update model parameters.

Returns

A dict of tensor for logging.

Return type

Dict[str, Tensor]

train_discriminator(inputs: dict, data_samples: List[mmedit.structures.EditDataSample], optimizer_wrapper: mmengine.optim.OptimWrapper) Dict[str, torch.Tensor][source]

Training function for discriminator. All GANs should implement this function by themselves.

Parameters
  • inputs (dict) – Inputs from dataloader.

  • data_samples (List[EditDataSample]) – Data samples from dataloader.

  • optim_wrapper (OptimWrapper) – OptimWrapper instance used to update model parameters.

Returns

A dict of tensor for logging.

Return type

Dict[str, Tensor]

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